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fastTS
View on CRAN: Click
here
Download and install fastTS package within the R console
Install from CRAN:
install.packages("fastTS")
Install from Github:
library("remotes")
install_github("cran/fastTS")
Install by package version:
library("remotes")
install_version("fastTS", "1.0.2")
Attach the package and use:
library("fastTS")
Maintained by
Ryan Andrew Peterson
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-02-07
Latest Update: 2024-02-07
Description:
An implementation of sparsity-ranked lasso and related methods
for time series data. This methodology is especially useful for
large time series with exogenous features and/or complex
seasonality. Originally described in Peterson and Cavanaugh
(2022) in the context of variable
selection with interactions and/or polynomials, ranked sparsity is
a philosophy with methods useful for variable selection in the
presence of prior informational asymmetry. This situation exists for time
series data with complex seasonality, as shown in Peterson and Cavanaugh
(2024) , which also describes this package
in greater detail. The sparsity-ranked penalization methods for time series
implemented in 'fastTS' can fit large/complex/high-frequency time series
quickly, even with a high-dimensional exogenous feature set. The method is
considerably faster than its competitors, while often producing more
accurate predictions. Also included is a long hourly series of arrivals
into the University of Iowa Emergency Department with concurrent local
temperature.
How to cite:
Ryan Andrew Peterson (2024). fastTS: Fast Time Series Modeling for Seasonal Series with Exogenous Variables. R package version 1.0.2, https://cran.r-project.org/web/packages/fastTS. Accessed 06 Jan. 2025.
Previous versions and publish date:
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Complete documentation for fastTS
Functions, R codes and Examples using
the fastTS R package
Some associated functions: fastTS . internal . predict.fastTS . uihc_ed_arrivals .
Some associated R codes: data.R . fastTS.R . helpers.R . prediction.R . Full fastTS package functions and examples
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